EP2948785B1 - Procédé pour déterminer un observateur d'état de charge relevant de la technique de régulation - Google Patents

Procédé pour déterminer un observateur d'état de charge relevant de la technique de régulation Download PDF

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EP2948785B1
EP2948785B1 EP14700888.2A EP14700888A EP2948785B1 EP 2948785 B1 EP2948785 B1 EP 2948785B1 EP 14700888 A EP14700888 A EP 14700888A EP 2948785 B1 EP2948785 B1 EP 2948785B1
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model
local
battery
soc
state
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EP2948785A1 (fr
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Christoph HAMETNER
Stefan Jakubek
Amra SULJANOVIC
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AVL List GmbH
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/367—Software therefor, e.g. for battery testing using modelling or look-up tables
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/382—Arrangements for monitoring battery or accumulator variables, e.g. SoC
    • G01R31/3828—Arrangements for monitoring battery or accumulator variables, e.g. SoC using current integration
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/382—Arrangements for monitoring battery or accumulator variables, e.g. SoC
    • G01R31/3842—Arrangements for monitoring battery or accumulator variables, e.g. SoC combining voltage and current measurements
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/392—Determining battery ageing or deterioration, e.g. state of health
    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
    • H01M10/00—Secondary cells; Manufacture thereof
    • H01M10/42—Methods or arrangements for servicing or maintenance of secondary cells or secondary half-cells
    • H01M10/48—Accumulators combined with arrangements for measuring, testing or indicating the condition of cells, e.g. the level or density of the electrolyte
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E60/00—Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
    • Y02E60/10—Energy storage using batteries

Definitions

  • the subject invention relates to a method for determining a control-technical observer for estimating the SoC of a battery.
  • SoC state of charge
  • the state of charge (SoC) of the battery or a battery cell depending on the measured variables in the vehicle such as loading or Discharge current, battery voltage and temperature, determined.
  • SoC control observer who uses a nonlinear battery model, which models the nonlinear battery behavior of the battery voltage as a function of the charge and discharge current.
  • the SoC observer estimates the actual state of charge of the battery on the basis of this model and the battery voltage measured in the vehicle. This is for example from the DE 10 2009 046 579 A1 which uses a simple equivalent electrical circuit as a model of the cell.
  • each model needs its own model.
  • models are difficult to parameterize (e.g., electrochemical models) and / or are only in a particular parameter range, e.g. only within a certain temperature range, trustworthy (e.g., equivalent circuit electrical models), and / or unsuitable for real-time applications due to their complexity, and thus for use in a battery management system.
  • a nonlinear model of the battery in the form of a local model network, is generated from the measured data of a previously determined optimized design using a data-based modeling method consisting of a number of local, linear, time-invariant, dynamic models that each have validity in certain ranges of the input quantities determined.
  • a local observer is determined for each local model of the model network determined in this way.
  • the control-technical observer for estimating the SoC then results from a linear combination of the local observers.
  • Such data-based modeling methods offer the advantage in this context that they are suitable for different battery types and also for real-time applications.
  • the process can run as automated as possible, so that the effort for the creation of the SoC observer can be reduced.
  • the SoC Observer setup process is based on a design that is performed on a real battery by real-world measurements, the resulting measurements and models will depend on the aging condition of the battery at the time of the measurements. Thus, the observer created for the SoC provides inaccurate estimates for the SoC for other aging conditions.
  • at least one input variable or at least one valid function of the local model network ie the nonlinear battery model, is scaled by at least one parameter for the aging state of the battery .
  • the SoC observer can provide good estimates for the SoC or determine the aging condition of the battery itself as an additional estimate.
  • An advantage of this methodology is therefore that the change in the state of aging can be taken into account via a single parameter.
  • a simple initial model of the battery or battery cell is determined.
  • the following are the terms battery and battery cell within the meaning of the invention considered equivalent. It is assumed that any known model of any battery cell.
  • the known battery model is eg a model known from another battery, a linear battery model, or a non-linear battery model.
  • a simple linear battery model may be determined by applying a current pulse to the battery or cell, thereby measuring the resulting voltage.
  • the application of known impedance spectroscopy could also be used to create a simple battery model. From these data, a linear battery model, eg a simple current-voltage relationship, can then be determined by means of identification methods. Although such a jump test applies only to a specific operating point of the battery or battery cell and only for the specific current pulse, such a simple output model is sufficient as a starting point for the inventive method, as described below.
  • the optimum excitation for the battery or battery cell is now determined by means of a model-based Design of Experiments (DoE) method.
  • DoE Design of Experiments
  • Common methods for experimental design for non-linear systems use, for example, amplitude-modulated, pseudo-random binary signals (APRB signals) to excite system dynamics.
  • APRB signals amplitude-modulated, pseudo-random binary signals
  • the SoC is directly dependent on the excitation signal (the load current of the battery cell), eg according to the relationship with l (t) the instantaneous load current, C n of the nominal cell capacity and ⁇ I (I) with Coulomb efficiency. Therefore, when using batteries, a different approach must be sought.
  • the DoE must also ensure that the excitation signal represents a suitable and sufficient excitation of the battery cell and at the same time, that the entire operating range of the SoC is covered.
  • the time for carrying out the battery tests is also preferably taken into account.
  • An optimal model-based DoE is often determined by means of the so-called Fisher information matrix I FIM and a judgment criterion, such as a D-optimal assessment criterion.
  • a judgment criterion such as a D-optimal assessment criterion.
  • the determinant J FIM of the Fisher information matrix I FIM is to be maximized.
  • D-optimal assessment criterion there are of course, other well-known evaluation criteria that could also be applied. This is well known and will not be explained here.
  • the Fisher information matrix I FIM is known to be based on the partial derivative of the model output y according to the model parameters ⁇ , in the form
  • the excitation signal is initially given based on a certain number of design points, which are respectively given by the load current and the SoC value.
  • the goal of the following optimization is an optimal sequential order of such design points to meet the excitation requirements mentioned above.
  • the sign of the load current I (as charging or discharging) and the duration of the load current I depends on the SoC to be reached and the previous SoC, as in Fig.1 shown.
  • Within the permissible parameter range 1 (the input space) starting from a certain current SoC 2, 3, a new SoC 4, 5 can only be achieved if the load current I either increases (charges) for a certain period of time or decreases for a certain period of time (unloaded) is.
  • the duration of these charging and discharging pulses and thus also the test time T depends directly on this temporally sequential sequence and the respective assignment of load current and SoC value of the design points.
  • the test time T can also be the subject of optimization and therefore of the optimum DoE. This can be achieved, for example, by a result function is minimized.
  • variables with the index "init” designate the corresponding quantities for the initial design plan (starting point of the optimization) and “opt" the optimized values, as a result of the optimization.
  • the weighting parameter 0 ⁇ ⁇ ⁇ 1 weights between accuracy and test time. The larger ⁇ becomes, the larger the information content of the excitation signal, while the reduction of the test time is weighted more, the smaller ⁇ is selected.
  • Such optimization problems can be solved, for example, by means of a heuristic optimization method (eg a simulated annealing method) or other known methods, as is well known.
  • an initial DoE (initial design) consisting of a number of temporally sequential design points is shown.
  • the test plan results from a suitably chosen sequence of the desired design points (defined by load current and SoC).
  • load current l 0.
  • This initial design was the starting point for a simulated annealing algorithm for the determination of an optimized DoE (optimized design plan), as in Figure 3 shown.
  • the test time results from the sequential sequence and the assignment of load current and SoC of the design points.
  • Figure 4 shows the improvement of the determinant J FIM of the Fisher information matrix I FIM by a factor of approximately five while shortening the test time T by approximately 7% when performing the optimization.
  • the optimized test plan essentially covers the entire operating range of the battery cell and essentially captures the entire non-linear dynamic behavior of the battery cell.
  • the design is then run on the battery cell test bench, i. the battery cell is subjected to the individual design points in the determined temporal sequence according to the experimental design, and measurements are made on the battery cell in order to detect state variables of the battery.
  • This can also be automated or semi-automated. From these measurements, a model of the battery cell or battery can then be determined automatically. In doing so, e.g. Current, voltage and temperature of the battery measured, whereby the initial design plan is assumed to be known at the beginning of the measurement.
  • LMS local model network
  • An LMN interpolates between local models, each of which is valid in certain operating ranges (or ranges of input variables).
  • each i-th local model LM i of the LMN can consist of two parts, namely a validity function ⁇ i and a model parameter vector ⁇ i .
  • the model parameter vector ⁇ i comprises all the parameters for the i-th model and the validity function ⁇ i defines the validity range of the i-th local model (within the input space).
  • the global model output ⁇ ( k ) eg the cell voltage U (k), then results from a linear combination with a weighting of the M local model outputs by the validity function ⁇ i , in the form
  • the non-linear system behavior of the battery is thus described by the LMN via locally valid, linear models. These submodels are each valid in a certain subrange (defined by current, SoC, temperature, etc.) of the entire work area.
  • the model parameter vector ⁇ i would then be, for example, [a U, 1 , a U, 2 ,..., A U, n , b 1, 0 , b 1, 1 ,... B 1, m , b T, 1 , bS oC, 1 , C 0 ].
  • U designates the voltage of the battery, I the current and T the temperature, which are known in the respective system order (k), (k-1), ... (kn) from the determined and carried out test plan as measured values and as training data to serve for model identification.
  • the result of the experimental design is thus used for the model identification, whereby the model parameter or the model parameter vector ⁇ i is determined by a well-known optimization, eg by the method of least squares, which minimizes the error between estimated output and measured output, or similar optimization methods becomes.
  • the result is a nonlinear model of the battery that predicts the output ⁇ (k), eg U (k), as a function of the input quantities, eg current I and temperature T, as well as past values of these quantities
  • a control-technical observer for the SoC can be determined, which is described here using the example of a fuzzy observer.
  • the use of a fuzzy observer allows a simple and automated parameterization of the non-linear state observer. Due to the stationary design of the local filters, a real-time implementation is possible because the computational effort in each time step is low (in contrast to eg Extended Kalman Filter).
  • Each local model of the LMN is a linear, time-invariant, dynamic system. Therefore, using standard calf filter theory, a local observer is created for each local linear state space model. A global filter then results from a linear combination of the local filters.
  • the nonlinear observer design therefore includes the description of the nonlinear system as local, linear state space models.
  • the state vector z (k-1) of the system is supplemented by the state SoC (k-1), which previously served as input for the determination of the LMN.
  • This results from a combination of the LMN with the relative SoC model from earlier in the mold SoC k SoC k - 1 + T S C n i k .
  • Ts denotes the sampling time.
  • a local Kalman filter with the gain matrix K i can be determined for each local model of the LMN in order to estimate the local state.
  • the estimated state ⁇ ( k ) containing the estimated SoC then follows from the equations With
  • the determination of the Kalman gain matrix K i takes place, for example, via the known discrete algebraic Riccati equation (DARE) with the covariance matrices, which measure the measurement noise and describe the process noise.
  • DARE discrete algebraic Riccati equation
  • the covariance value describing the process noise of the SoC in the covariance matrices can, however, also be considered and used as an adjustable tuning parameter for the nonlinear SoC observer, as shown by the Fig.7 and 8 shown.
  • the two Figs. 7 and 8 show the function of the control-technical observer. In both figures it can be seen that the estimate of the SoC (in each case the dashed curve diagram) approaches the actual value of the SoC even with arbitrary output data.
  • Figure 8 The weighting of the SoC has been increased in the state, resulting in faster convergence, but a slightly increased error.
  • the determined control-technical observer for the estimation of the SoC can be explained as in Fig. 5 shown integrated into a battery management system 11.
  • the load current I of a battery 10 generates a battery voltage U and a battery temperature T, which are supplied to the battery management system 11 as measured variables.
  • the extended state space model 12 determined according to the invention and the nonlinear observer 13, which estimates the current SoC is implemented.
  • the estimated SoC can then be used accordingly, for example in the battery management unit 11 itself, or in a battery or vehicle control unit, not shown.
  • the determination of the state of health is important in order to be able to specify a criterion for the quality of the battery 10.
  • SoH state of health
  • the aging state SoH of a battery 10 is indicated by a characteristic, such as the ratio of the available capacity C akt of the battery compared to the known nominal capacity C init of the battery, ie C ak t / C init .
  • the SoH In order to enable an efficient use of the battery 10 and thus to achieve the longest possible life, preferably the SoH, or a characteristic for it, should be determined as accurately as possible, for example, by the battery management unit 11, since the current aging state SoH and the determination of the State of charge SoC influenced. For an exact determination of the state of charge SoC an aged battery 10, it is therefore advantageous if the respective current aging state SoH is taken into account in the determination of the state of charge SoC. It will now be described how the above-described SoC observer can be supplemented to take into account or determine the state of aging SoH.
  • the goal here is also to reproduce the dynamic behavior of the output variable y (k), for example the battery voltage U (k), of aging batteries 10 with the LMN or the state space model 12 derived therefrom by adapting these influencing variables and thus providing an accurate estimate to enable the SoC.
  • the aging state SoH can thereby be determined as a further variable, output and used further.
  • the parameter for the aging state SoH is the available capacitance C akt , or the ratio of the available capacitance C akt in comparison to the nominal capacitance C init , ie C akt / C init .
  • it may also be the current internal resistance R act or the ratio of the actual internal resistance of the aged battery R akt in comparison to the known internal resistance of a new battery R init , ie R ini t / R akt , or a combination thereof, as a parameter for the aging state SoH. be taken into account.
  • a battery model in the form of the LMN can then be adapted by specifying the parameter, eg C akt / C init and / or R akt / R init , in order to determine the behavior of the output variable y (k) for known input variables x (k) for any, to simulate resulting aging conditions SoH, as in Figure 9 indicated.
  • the effect of this parameter (s) on at least one model parameter in the model parameter vector ⁇ i and optionally also on the validity functions ⁇ i , and / or converted to at least one input variable x (k), whereby an adaptation of the static and dynamic behavior of the nonlinear Battery model is done.
  • the state of charge SoC can also be determined as a function of the current state of aging SoH. This will be explained below using the example above.
  • the model parameters such as a U, 1 , a U, 2 ,..., A U, n , b l, 0 , b l, i , .. b l, m , b T, 1 , b SoC, 1 , C 0 , which determine the model parameter vector ⁇ i or the system matrix A i in the state space model 12, are changed accordingly to take into account the aging of the battery 10 in the battery model.
  • the input variables x (k) of the local model network LMN or the state variables in the state space model 12 can also be changed.
  • the non-linear battery model LMN or the state space model 12 can be adapted to the current state of aging.
  • the concrete way of scaling depending on the Characteristic is not decisive here, but there are of course many possibilities how to scale.
  • the parameter for the aging state SoH ie, for example, the capacitance C act or the internal resistance R act , but is usually not directly measurable and therefore not known.
  • This characteristic, or the associated aging state SoH should therefore be estimated by the nonlinear observer.
  • the above-described non-linear observer 13 is therefore now extended by the estimation of the state of aging SoH, or a characteristic thereof.
  • This combined SoH / SoC observer is based on the extended nonlinear battery model in the form of the local model network LMN or the derived extended state space model 12.
  • the characteristic such as C act , C act / C init , R act or R act / R init , thereby directly or indirectly influences input variables and / or model parameters of this state space model, as described above.
  • SoC and SoH are estimated at the same time.
  • An important point is the consideration of the different time constants for the observation of SoC and SoH, since the SoH usually changes much slower than the SoC.
  • the cascaded observer there is an inner observer (circle) which aims to determine the SoC as accurately as possible and which is described above.
  • An external observer 14 building on the inner observer 13 then presents an overall estimator for the SoH characteristic, as in FIG Figure 10 shown.
  • the advantage of the cascaded observer is that the different time constants for the two observers are already indirectly taken into account via the architecture.
  • the SoH estimator can thus be operated by the separation into an inner and outer observer also with a much lower sampling time. The cascaded observer is therefore preferred.
  • Figure 10 is the observer off Figure 5 by an observer 14 for the aging state SoH, or a characteristic for this, as here zB act , in cascaded execution been added.
  • the determined extended state space model 12, supplemented by the above-described consideration of the parameter for the aging state SoH, and the non-linear observer 13 for the state of charge SoC are again provided.
  • Added is a Observer 14 for the estimation of the parameter of the state of aging, here the current capacity ⁇ act .
  • an observation error e with respect to the parameter is minimized.
  • the observation error e is determined, for example, from the difference between the measured output variable y, here the battery voltage U, and the output quantity ⁇ calculated by the non-linear battery model or the state space model 12, here the estimated battery voltage ⁇ .
  • the goal of the estimation / optimization in the observer 14 is now to minimize a certain quality measure with respect to the observation error e.
  • the output variable ⁇ is calculated for different capacitances C akt , eg between a predetermined C min and a C max , from the nonlinear battery model or the state space model 12 and the SoC is estimated and the observation error e is determined therefrom with the measured output variable U belonging to the respective time. From this, the capacitance C act can then be determined via the quality measure, which minimizes the observation error e according to the quality measure. Thus, by adapting C act , a certain measure of the observational error e is minimized, which ultimately results in determining the capacitance C act which best explains the behavior of the battery 10.
  • boundary conditions and secondary conditions can also be taken into account, for example monotonic decay of the capacitance C act over time, since the battery 10 can only get older, but not younger.
  • the combined observer thus generally aims to minimize the deviations of the estimated states from the actual values, taking into account marginal and constraints in the form of equality or inequality constraints. These constraints can be used, for example, to limit or penalize rate of change of capacitance, the direction of SoH correction, etc.
  • constraints can be used, for example, to limit or penalize rate of change of capacitance, the direction of SoH correction, etc.
  • optimization method can be used different, but known, optimization method.
  • the thus determined observer 14 can then determine the SoH, or a characteristic of the SoH.
  • Figure 11 is the characteristic value C akt estimated with the combined SoH / SoC observer, based on the rated capacity C init of a battery, as a function of the number of full charge / discharge cycles Z (with crosses). Dots show the actually measured capacity drop. From this it can be seen that the state of aging SoH, or a parameter for it, can be estimated very well by the described method.
  • the SoH estimate can be much slower than the SoC estimate.
  • the observer for the SoC always takes into account the current estimate for the SoH, or the parameter for it.
  • the observer 14 for the aging state SoH would be integrated into the observer 13 for the SoC.

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Claims (9)

  1. Procédé de détermination d'un observateur d'ingénierie de régulation (13) destiné à estimer l'état de charge Soc d'une batterie (10), caractérisé en ce que le procédé comprend les étapes suivantes consistant à :
    - prescrire un signal d'excitation de sortie de la batterie (10), le signal d'excitation étant constitué d'une séquence temporelle d'un grand nombre de points de plan définis par le courant de charge (I) et la valeur SoC,
    - prescrire un modèle de sortie de la batterie (10), comportant une sortie de modèle (y) et des paramètres de modèle (ψ),
    - déterminer un plan d'expériences optimisé sous la forme d'une séquence temporelle optimisée des points de plan au moyen d'une méthode de Plan d'Expériences (Design of Expérience) basée sur un modèle à partir de la matrice d'information de Fisher (IFIM) définie par I FIM = 1 σ 2 ∑ k = 1 N ∂ y k ψ ∂ ψ ∂ y k ψ T ∂ ψ
    Figure imgb0026
     et d'un critère de jugement,
    - appliquer à la batterie (10) les points de plan individuels dans la séquence temporelle selon le plan d'expériences déterminé, des valeurs de mesure de grandeurs d'état de la batterie (10) étant détectées,
    - déterminer un modèle non linéaire de la batterie (10) sur la base des valeurs de mesure déterminées et par référence à un réseau de modèles locaux constitué d'un certain nombre de modèles (LMi) dynamiques, temporellement invariants, linéaires, locaux qui présentent chacun une validité dans des domaines déterminées des grandeurs d'entrée, la sortie de modèle étant déterminée sous forme d'une combinaison linéaire pondérée des sorties des modèles locaux (LMi),
    - convertir les modèles locaux (LMi) du réseau de modèles en modèles d'espace d'états linéaires locaux, avec un vecteur d'état qui contient le SoC,
    - créer un observateur local pour chaque modèle d'espace d'état linéaire local et
    - créer l'observateur d'ingénierie de régulation (13) à partir d'une combinaison linéaire des observateurs locaux.
  2. Procédé selon la revendication 1, caractérisé en ce que le plan d'expérience optimisé est déterminé par minimisation d'une fonction de résultat J opt = α J FIM , init J FIM , opt + 1 − α T opt T init ,
    Figure imgb0027
     JFIM étant le déterminant de la matrice d'information de Fisher, et α étant un facteur de pondération.
  3. Procédé selon la revendication 1, caractérisé en ce que chaque i-ème modèle local (LMi) du réseau de modèles locaux (LMN) est constitué d'une fonction de validité (Φi) et d'un vecteur de paramètre de modèle θi, le vecteur de paramètre de modèle (θi) comportant tous les paramètres de modèle pour le i-ème modèle (LMi) et la fonction de validité (Φi) définissant le domaine de validité du i-ème modèle local (LMi) et une valeur d'estimation locale de la grandeur de sortie ŷi (k) du i-ème modèle local (LMi) étant déterminée à l'instant k à partir de ŷi (k) =xT(k)θi, x(k) représentant un vecteur de régression qui contient des entrées et sorties présentes et passées, en ce que la sortie de modèle global ŷ(k) est déterminée à partir d'une combinaison linéaire avec une pondération des sorties de modèles locaux par la fonction de validité (Φi) conformément à y k = ∑ i = 1 M Φ i k y ^ i k
    Figure imgb0028
     et en ce que le vecteur de paramètres de modèle (θi) est déterminé au moyen d'une optimisation qui minimise l'erreur entre la grandeur de sortie estimée et la grandeur de sortie mesurée.
  4. Procédé selon la revendication 3, caractérisé en ce qu'un vecteur d'état zi(k)=Aiz(k-1)+Biu(k) est évalué pour chaque i-ème modèle local (LMi), la matrice de système Ai et la matrice d'entrée Bi étant obtenues à partir du vecteur de paramètre de modèle (θi) déterminé et tout l'état de système étant obtenu par pondération des états locaux avec la fonction de validité (Φi) à z(k) = ∑ i = 1 M Φ i k − 1 A i z k − 1 + B i u k
    Figure imgb0029
     et la sortie de modèle global selon y(k)=Cz(k), avec C=[1 0...0 0], et en ce qu'un filtre de Kalman local est déterminé pour chaque état local avec la matrice de gain Ki pour estimer l'état local.
  5. Procédé selon la revendication 4, caractérisé en ce que l'état estimé ẑ(k), qui contient le SoC estimé, est obtenu à partir de l'équation z ^ k = ∑ i = 1 M Φ i ( k −
    Figure imgb0030
     1 ) z i * k + K i y k − y ^ k
    Figure imgb0031
     avec z i * k = A i z ^ k − 1 + B i u k
    Figure imgb0032
     et ŷ(k) = ∑ i = 1 M Φ i k − 1 Cz i * k .
    Figure imgb0033
  6. Procédé selon l'une des revendications 1 à 5, caractérisé en ce qu'au moins une grandeur d'entrée du réseau de modèles locaux (LMN) est cadrée par au moins une grandeur caractéristique (Cakt, Rakt) de l'état de vieillissement (SoH) de la batterie (10).
  7. Procédé selon l'une des revendications 3 à 6, caractérisé en ce qu'au moins un paramètre de modèle du réseau de modèles locaux (LMN) est cadré par au moins une grandeur caractéristique (Cakt, Rakt) de l'état de vieillissement (SoH) de la batterie (10).
  8. Procédé selon la revendication 6 ou 7, caractérisé en ce que l'observateur (13) pour l'état de charge SoC est étendu d'un observateur (14) pour la grandeur caractéristique (Cakt, Rakt).
  9. Procédé selon la revendication 8, caractérisé en ce qu'une erreur d'observation (e) entre la grandeur de sortie mesurée (y) et la grandeur de sortie estimée (y) du modèle de batterie non linéaire est minimisée par l'observateur (14) pour SoH à l'aide d'une mesure de qualité prédéterminée de l'erreur d'observation (e) par rapport à la grandeur (Cakt, Rakt).
EP14700888.2A 2013-01-23 2014-01-17 Procédé pour déterminer un observateur d'état de charge relevant de la technique de régulation Active EP2948785B1 (fr)

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ATA50046/2013A AT512003A3 (de) 2013-01-23 2013-01-23 Verfahren zur Ermittlung eines regelungstechnischen Beobachters für den SoC
ATA50736/2013A AT513189B1 (de) 2013-01-23 2013-11-06 Verfahren zur Ermittlung eines regelungstechnischen Beobachters für den SoC
PCT/EP2014/050905 WO2014114564A1 (fr) 2013-01-23 2014-01-17 Procédé pour déterminer un observateur d'état de charge relevant de la technique de régulation

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AT513189A2 (de) 2014-02-15
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US10338146B2 (en) 2019-07-02
JP6404832B2 (ja) 2018-10-17
CN105008946B (zh) 2017-12-22
JP2016513238A (ja) 2016-05-12
WO2014114564A1 (fr) 2014-07-31
CN105008946A (zh) 2015-10-28
AT513189B1 (de) 2014-11-15
AT512003A2 (de) 2013-04-15
KR102142745B1 (ko) 2020-08-10
AT512003A3 (de) 2014-05-15
KR20150111961A (ko) 2015-10-06

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